The use of continuous glucose monitoring combined with computer-based eMPC algorithm for tight glucose control in cardiosurgical ICU

Intensive or liberal glucose control in intensive care units for septic patients? A meta-analysis of randomized controlled trials

OBJECTIVE

To compare the clinical outcomes of intensive glucose control and liberal glucose control for septic patients in intensive care unit.

METHODS

The databases of PubMed, Cochrane Library, Embase and Web of Science were searched systematically from inception to November 27, 2023 to identify trials involving a randomized comparison between intensive and liberal glucose control for septic patients in intensive care unit.

RESULTS

A total of 14 randomized controlled trials involving 6226 patients were finally included. There was no statistically significant difference observed between intensive glucose control and liberal glucose control in terms of all-cause mortality, the need for renal replacement, vasopressor-free and mechanical ventilation-free days, and length of hospital stay. However, it is noteworthy that intensive glucose control exhibited a statistically higher risk of severe hypoglycemia (RR 2.66; 95%CI 1.85 to 3.83), need for blood transfusion (RR 1.12; 95%CI 1.01 to 1.23), and statistically prolonged length of stay in the ICU (MD 1.67; 95%CI 0.22 to 3.12) compared to liberal glucose control. Nevertheless, sensitivity analysis revealed that the need for blood transfusion and length of stay in the intensive care unit were not robust.

CONCLUSIONS

Both intensive and liberal glucose control had comparable effects on improving patient outcomes, but intensive glucose control carried a higher risk of severe hypoglycemia.

Intermittent scanning continuous glucose monitoring is safe and useful in postsurgical glucose monitoring after pancreatoduodenectomy

AIMS

Intermittently scanned continuous glucose monitoring (isCGM) systems have not been thoroughly evaluated during in-hospital stay, and there are concerns about accuracy during various conditions. Patients undergoing pancreatoduodenectomy have an increased risk of hyperglycaemia after surgery which is aggravated by parenteral nutrition therapy. This study aims to evaluate glycaemic control and safety during insulin infusion in a surgical non-ICU ward, using a hybrid glucose monitoring approach with isCMG and periodic point-of-care (POC) testing.

METHODS

We prospectively included 100 patients with a resectable pancreatic tumour. After surgery, continuous insulin infusion was initiated when POC glucose was > 7 mmol/l and titrated to maintain glucose between 7 and 10 mmol/l. Glucose was monitored with isCGM together with intermittent POC, every 3-6 h. Median absolute relative difference (MARD) and hypoglycaemic events were evaluated. Mean glucose was compared with a historic control (n = 100) treated with multiple subcutaneously insulin injections, monitored with POC only.

RESULTS

The intervention group (isCGM/POC) had significantly lower POC glucose compared with the historic control group (8.8 ± 2.2 vs. 10.4 ± 3.4 mmol/l, p < 0.001). MARD was 17.8% (IQR 10.2-26.7). isCGM readings were higher than POC measurements in 91% of the paired cases, and isCGM did not miss any hypoglycaemic event. About 4.5% of all isCGM readings were < 3.9 mmol/l, but only six events were confirmed with POC, and none was < 3.0 mmol/l.

CONCLUSIONS

A hybrid approach with isCGM/POC is a safe and effective treatment option in a non-ICU setting after pancreatoduodenectomy.

Clinical relevance of glucose metrics during the early brain injury period after aneurysmal subarachnoid hemorrhage: An opportunity for continuous glucose monitoring

Hyperglycaemia, hypoglycaemia and higher glucose variability during the Early Brain Injury (EBI) period of aneurysmal subarachnoid hemorrhage (aSAH) have been associated with poor clinical outcome. However, it is unclear whether these associations are due to direct glucose-driven injury or if hyperglycaemia simply acts as a marker of initial severity. Actually, strict glucose control with intensive insulin therapy has not been demonstrated as an effective strategy for improving clinical outcomes after aSAH. Currently published studies describing an association between hyperglycaemia and prognosis in aSAH patients have been based on isolated glucose measurements and did not incorporate comprehensive dynamic evaluations, such as those derived from subcutaneous continuous glucose monitoring devices (CMG). Arguably, a more accurate knowledge on glycaemic patterns during the acute phase of aSAH could increase our understanding of the relevance of glycaemia as a prognostic factor in this disease as well as to underpin its contribution to secondary focal and diffuse brain injury. Herein, we have summarized the available evidence on the diagnostic and prognostic relevance of glucose metrics during the acute phase of cerebrovascular diseases, focusing in the EBI period after aSAH. Overall, obtaining a more precise scope of acute longitudinal glucose profiles could eventually be useful for improving glucose management protocols in the setting of acute aSAH and to advance toward a more personalized management of aSAH patients during the EBI phase.

Subcutaneous continuous glucose monitoring in critically ill patients during insulin therapy: a meta-analysis

BACKGROUND

Using continuous glucose monitoring (CGM) in critically ill adult patients requiring insulin therapy has increased with inconsistent results. Thus, we conducted a meta-analysis to assess the effect of CGM and frequent point-of-care (POC) measurements in such a patient population.

METHODS

We searched PubMed, Embase, Cochrane Library, China national knowledge infrastructure, and Wanfang for relevant articles from inception to Jan 15, 2022. Randomized controlled trials (RCTs) were considered if they focused on critically ill patients who required insulin and were treated with CGM or any POC measurements. We used the Cochrane risk evaluating tool to assess study quality. Subgroup analysis and publication bias were also conducted.

RESULTS

We finally included 19 RCTs with 1,852 participants. The quality of the included studies were at a low to moderate levels. Overall, CGM devices significantly reduced hypoglycemia incidence (Risk ratio (RR) 0.35; 95% CI, 0.25-0.49; P<0.00001) than the POC measurement. Further subgroup and sensitivity analyses confirmed this result. The CGM group also had lower overall mortality (RR 0.54; 95% CI, 0.34-0.86; P=0.01), lower glucose variability, and nosocomial infection. The time in, below, or above target blood glucose range, insulin use, and length of stay in the ICU were comparable between the two groups. In addition, few studies provided data in favor of decreased nursing workload and medical costs in the CGM group.

CONCLUSIONS

The CGM technique could significantly reduce hypoglycemia incidence, overall mortality, and glucose variability compared to POC measurement in critically ill patients. However, further large, well-designed RCTs are required to confirm our results.

Stress-Induced Hyperglycemia: Consequences and Management

Hyperglycemia during stress is a common occurrence seen in patients admitted to the hospital. It is defined as a blood glucose level above 180mg/dl in patients without pre-existing diabetes. Stress-induced hyperglycemia (SIH) occurs due to an illness that leads to insulin resistance and decreased insulin secretion. Such a mechanism causes elevated blood glucose and produces a complex state to manage with external insulin. This article compiles various studies to explain the development and consequences of SIH in the critically ill that ultimately lead to an increase in mortality while also discussing the dire impact of SIH on certain acute illnesses like myocardial infarction and ischemic stroke. It also evaluates multiple studies to understand the management of SIH with insulin and proper nutritional therapy in the hospitalized patients admitted to the Intensive care unit (ICU) alongside the non-critical care unit. While emphasizing the diverse effects of improper control of SIH in the hospital, this article elucidates and discusses the importance of formulating a discharge plan due to an increased risk of type 2 diabetes in the recovered. 

Use of Continuous Glucose Monitor in Critically Ill COVID-19 Patients Requiring Insulin Infusion: An Observational Study

CONTEXT

The coronavirus disease 2019 (COVID-19) pandemic has created a need for remote blood glucose (BG) monitoring in the intensive care unit (ICU).

OBJECTIVE

To evaluate feasibility and patient safety of a hybrid monitoring strategy of point-of-care (POC) BG plus continuous glucose monitor (CGM) in the ICU.

DESIGN

Retrospective analysis.

SETTING

ICU of an academic medical center.

PATIENTS

Patients with COVID-19 on IV insulin.

INTERVENTION

After meeting initial validation criteria, CGM was used for IV insulin titration and POC BG was performed every 6 hours or as needed.

MAIN OUTCOME MEASURES

Outcomes included frequency of POC BG, workflow, safety, and accuracy measures.

RESULTS

The study included 19 patients, 18 with CGM data, mean age 58 years, 89% on mechanical ventilation, 37% on vasopressors, and 42% on dialysis. The median time to CGM validation was 137 minutes (interquartile range [IQR] 114-206). During IV insulin, the median number of POC values was 7 (IQR 6-16) on day 1, and declined slightly thereafter (71% reduction compared with standard of 24/day). The median number of CGM values used nonadjunctively to titrate IV insulin was 11.5 (IQR 0, 15) on day 1 and increased thereafter. Time in range 70 to 180 mg/dL was 64 ± 23% on day 1 and 72 ± 16% on days 2 through 7, whereas time <70 mg/dL was 1.5 ± 4.1% on day 1 and <1% on days 2 through 7.

CONCLUSIONS

This study provides data to support that CGM using a hybrid protocol is feasible, accurate, safe, and has potential to reduce nursing and staff workload.

The development of a glucose prediction model in critically ill patients

PURPOSE

The aim of the current study is to develop a prediction model for glucose levels applicable for all patients admitted to the ICU with an expected ICU stay of at least 24 h. This model will be incorporated in a closed-loop glucose system to continuously and automatically control glucose values.

METHODS

Data from a previous single-center randomized controlled study was used. All patients received a FreeStyle Navigator II subcutaneous CGM system from Abbott during their ICU stay. The total dataset was randomly divided into a training set and a validation set. A glucose prediction model was developed based on historical glucose data. Accuracy of the prediction model was determined using the Mean Squared Difference (MSD), the Mean Absolute Difference (MAD) and a Clarke Error Grid (CEG).

RESULTS

The dataset included 94 ICU patients with a total of 134,673 glucose measurements points that were used for modelling. MSD was 0.410 ± 0.495 for the model, the MAD was 5.19 ± 2.63 and in the CEG 99.8% of the data points were in the clinically acceptable regions.

CONCLUSION

In this study a glucose prediction model for ICU patients is developed. This study shows that it is possible to accurately predict a patient's glucose 30 min ahead based on historical glucose data. This is the first step in the development of a closed-loop glucose system.

Effectiveness and safety of the Space GlucoseControl system for glycaemia control in caring for postoperative cardiac surgical patients

BACKGROUND

Hyperglycaemia is a very common complication in post-cardiac surgical patients, and as such, it must be properly managed. For this purpose, the enhanced Model Predictive Control algorithm for glycaemia control has been implemented into a nurse-led device called Space GlucoseControl (SGC) that aims to achieve a safe and effective blood glucose control in a better way than the traditional "paper-based" protocols.

PURPOSE

The aim of the study was to know the effectiveness and safety of the SGC in glycaemia control in cardiosurgical adult patients in the immediate postoperative period in the intensive care unit.

METHODS

A prospective before-and-after intervention study was conducted. One hundred sixty cardiosurgical adult patients with hyperglycaemia were selected: 80 in the control group from May to November 2018 and 80 in the intervention group (use of the SGC device) from January to December 2019. The primary outcome was the percentage of time within the target range (140-180 mg/dL in the control group and 100-160 mg/dL in the intervention group).

RESULTS

The percentage of time within the target range was significantly higher in the SGC group than in the control group (70.5% [58.25-80] vs 54.83% [36.09-75], p < 0.001). The range was also achieved earlier with the SGC (5 [3-6.875] hours vs 7 [4-11] hours; p < 0.05). The first blood glucose value after reaching the target range was higher in the control group, with statistical significance (p < 0.05). There were no hypoglycaemia episodes in the control group. However, during SGC treatment, six episodes of hypoglycaemia occurred, and all of them were nonsevere (mean value = 61 mg/dL).

CONCLUSION

The SGC is useful to achieve a faster tight glycaemic control, with a higher percentage of time within the target range, although episodes of nonsevere hypoglycaemia could be observed.

Intermittent Use of Continuous Glucose Monitoring: Expanding the Clinical Value of CGM

In addition to the continuous use, the intermittent use of continuous glucose monitoring (CGM) is an application of CGM, expanding the typical medical use cases. There are a variety of reasons and occasions that speak in favor of using CGM only for a limited time. To date, these circumstances have not been sufficiently discussed. In this article, we define discontinuous or intermittent CGM use, provide reasons for using it, and expand on the benefits and possibilities of using CGM on a temporary basis. We aim to draw attention to this important topic in the discussion of CGM use and give examples for a different method of CGM use. As well, we would like to foster the allocation of CGM to the right patient groups and indications, especially in cases of limited resources. From a global point of view, intermittent CGM use is more likely to occur than continuous use, primarily for economic reasons.

Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU

Objective

Glycemic control is an important component of critical care. We present a data-driven method for predicting intensive care unit (ICU) patient response to glycemic control protocols while accounting for patient heterogeneity and variations in care.

Materials and Methods

Using electronic medical records (EMRs) of 18 961 ICU admissions from the MIMIC-III dataset, including 318 574 blood glucose measurements, we train and validate a gradient boosted tree machine learning (ML) algorithm to forecast patient blood glucose and a 95% prediction interval at 2-hour intervals. The model uses as inputs irregular multivariate time series data relating to recent in-patient medical history and glycemic control, including previous blood glucose, nutrition, and insulin dosing.

Results

Our forecasting model using routinely collected EMRs achieves performance comparable to previous models developed in planned research studies using continuous blood glucose monitoring. Model error, expressed as mean absolute percentage error is 16.5%–16.8%, with Clarke error grid analysis demonstrating that 97% of predictions would be clinically acceptable. The 95% prediction intervals achieve near intended coverage at 93%–94%.

Discussion

ML algorithms built on observational data sources, such as EMRs, present a promising approach for personalization and automation of glycemic control in critical care. Future research may benefit from applying a combination of methodologies and data sources to develop robust methodologies that account for the variations seen in ICU patients and difficultly in detecting the extremes of observed blood glucose values.

Conclusion

We demonstrate that EMRs can be used to train ML algorithms that may be suitable for incorporation into ICU decision support systems.

A standardized glucose-insulin-potassium infusion protocol in surgical patients: Use of real clinical data from a clinical data warehouse

AIMS

We evaluated the clinical usefulness of a new unified glucose-insulin-potassium (GIK) regimen in a general surgical department.

METHODS

Surgical patients treated under the previous diverse GIK regimens (September 2016 to August 2017) and the new unified GIK regimen (September 2017 to August 2018) were identified in records of the Clinical Data Warehouse of Seoul National University Bundang Hospital. Serial and area under the curve (AUC) glucose levels, and percentages of time within the target glucose levels were compared in propensity score matched patients in the diverse GIK regimen and in the unified GIK regimen (n = 227 in each group).

RESULTS

The AUC of glucose at 6 h and 12 h was lower under the unified GIK regimen than the diverse GIK regimen. The percentage of target glucose levels was higher in the unified GIK regimen compared to the diverse GIK regimen (81.5% vs. 75.0%, P = 0.026), but the occurrence of hypoglycaemia did not differ significantly between groups.

CONCLUSIONS

The unified GIK regimen was more effective than the diverse GIK regimen for glycaemic control and did not increase the number of patients developing hypoglycaemia. This validated written GIK regimen can be safely used in a general surgical department.

Continuous Glucose Monitoring in the Hospital

Continuous glucose monitors (CGMs) have suddenly become part of routine care in many hospitals. The coronavirus disease 2019 (COVID-19) pandemic has necessitated the use of new technologies and new processes to care for hospitalized patients, including diabetes patients. The use of CGMs to automatically and remotely supplement or replace assisted monitoring of blood glucose by bedside nurses can decrease: the amount of necessary nursing exposure to COVID-19 patients with diabetes; the amount of time required for obtaining blood glucose measurements, and the amount of personal protective equipment necessary for interacting with patients during the blood glucose testing. The United States Food and Drug Administration (FDA) is now exercising enforcement discretion and not objecting to certain factory-calibrated CGMs being used in a hospital setting, both to facilitate patient care and to obtain performance data that can be used for future regulatory submissions. CGMs can be used in the hospital to decrease the frequency of fingerstick point of care capillary blood glucose testing, decrease hyperglycemic episodes, and decrease hypoglycemic episodes. Most of the research on CGMs in the hospital has focused on their accuracy and only recently outcomes data has been reported. A hospital CGM program requires cooperation of physicians, bedside nurses, diabetes educators, and hospital administrators to appropriately select and manage patients. Processes for collecting, reviewing, storing, and responding to CGM data must be established for such a program to be successful. CGM technology is advancing and we expect that CGMs will be increasingly used in the hospital for patients with diabetes.

Facilitators and Barriers to Nursing Implementation of Continuous Glucose Monitoring (CGM) in Critically Ill Patients With COVID-19

OBJECTIVE

We describe our implementation of a continuous glucose monitoring (CGM) guideline to support intravenous insulin administration and reduce point of care (POC) glucose monitoring frequency in the coronavirus disease 2019 medical intensive care unit (MICU) and evaluate nurses' experience with implementation of CGM and hybrid POC + CGM protocol using the Promoting Action on Research in Health Services framework.

METHODS

A multidisciplinary team created a guideline providing criteria for establishing initial sensor-meter agreement within each individual patient followed by hybrid use of CGM and POC. POC measures were obtained hourly during initial validation, then every 6 hours. We conducted a focus group among MICU nurses to evaluate initial implementation efforts with content areas focused on initial assessment of evidence, context, and facilitation to identify barriers and facilitators. The focus group was analyzed using a qualitative descriptive approach.

RESULTS

The protocol was integrated through a rapid cycle review process and ultimately disseminated nationally. The Diabetes Consult Service performed device set-up and nurses received just-in-time training. The majority of barriers centered on contextual factors, including limitations of the physical environment, complex device set-up, hospital firewalls, need for training, and CGM documentation. Nurses' perceived device accuracy and utility were exceptionally high. Solutions were devised to maximize facilitation and sustainability for nurses while maintaining patient safety.

CONCLUSION

Outpatient CGM systems can be implemented in the MICU using a hybrid protocol implementation science approach. These efforts hold tremendous potential to reduce healthcare worker exposure while maintaining glucose control during the COVID-19 pandemic.

Continuous Glucose Monitoring in Critically Ill Patients With COVID-19: Results of an Emergent Pilot Study

BACKGROUND

Amidst the coronavirus disease 2019 (COVID-19) pandemic, continuous glucose monitoring (CGM) has emerged as an alternative for inpatient point-of-care blood glucose (POC-BG) monitoring. We performed a feasibility pilot study using CGM in critically ill patients with COVID-19 in the intensive care unit (ICU).

METHODS

Single-center, retrospective study of glucose monitoring in critically ill patients with COVID-19 on insulin therapy using Medtronic Guardian Connect and Dexcom G6 CGM systems. Primary outcomes were feasibility and accuracy for trending POC-BG. Secondary outcomes included reliability and nurse acceptance. Sensor glucose (SG) was used for trends between POC-BG with nursing guidance to reduce POC-BG frequency from one to two hours to four hours when the SG was in the target range. Mean absolute relative difference (MARD), Clarke error grids analysis (EGA), and Bland-Altman (B&A) plots were calculated for accuracy of paired SG and POC-BG measurements.

RESULTS

CGM devices were placed on 11 patients: Medtronic (n = 6) and Dexcom G6 (n = 5). Both systems were feasible and reliable with good nurse acceptance. To determine accuracy, 437 paired SG and POC-BG readings were analyzed. For Medtronic, the MARD was 13.1% with 100% of readings in zones A and B on Clarke EGA. For Dexcom, MARD was 11.1% with 98% of readings in zones A and B. B&A plots had a mean bias of -17.76 mg/dL (Medtronic) and -1.94 mg/dL (Dexcom), with wide 95% limits of agreement.

CONCLUSIONS

During the COVID-19 pandemic, CGM is feasible in critically ill patients and has acceptable accuracy to identify trends and guide intermittent blood glucose monitoring with insulin therapy.

Continuous Glucose Monitors and Automated Insulin Dosing Systems in the Hospital Consensus Guideline

This article is the work product of the Continuous Glucose Monitor and Automated Insulin Dosing Systems in the Hospital Consensus Guideline Panel, which was organized by Diabetes Technology Society and met virtually on April 23, 2020. The guideline panel consisted of 24 international experts in the use of continuous glucose monitors (CGMs) and automated insulin dosing (AID) systems representing adult endocrinology, pediatric endocrinology, obstetrics and gynecology, advanced practice nursing, diabetes care and education, clinical chemistry, bioengineering, and product liability law. The panelists reviewed the medical literature pertaining to five topics: (1) continuation of home CGMs after hospitalization, (2) initiation of CGMs in the hospital, (3) continuation of AID systems in the hospital, (4) logistics and hands-on care of hospitalized patients using CGMs and AID systems, and (5) data management of CGMs and AID systems in the hospital. The panelists then developed three types of recommendations for each topic, including clinical practice (to use the technology optimally), research (to improve the safety and effectiveness of the technology), and hospital policies (to build an environment for facilitating use of these devices) for each of the five topics. The panelists voted on 78 proposed recommendations. Based on the panel vote, 77 recommendations were classified as either strong or mild. One recommendation failed to reach consensus. Additional research is needed on CGMs and AID systems in the hospital setting regarding device accuracy, practices for deployment, data management, and achievable outcomes. This guideline is intended to support these technologies for the management of hospitalized patients with diabetes.

Pharmacotherapeutic considerations for the management of diabetes mellitus among hospitalized COVID-19 patients

INTRODUCTION

Diabetes mellitus is one of the most prevalent comorbidities identified in patients with coronavirus disease 2019 (COVID-19). This article aims to discuss the pharmacotherapeutic considerations for the management of diabetes in hospitalized patients with COVID-19.

AREAS COVERED

We discussed various aspects of pharmacotherapeutic management in hospitalized patients with COVID-19: (i) susceptibility and severity of COVID-19 among individuals with diabetes, (ii) glycemic goals for hospitalized patients with COVID-19 and concurrent diabetes, (iii) pharmacological treatment considerations for hospitalized patients with COVID-19 and concurrent diabetes.

EXPERT OPINION

The glycemic goals in patients with COVID-19 and concurrent type 1 (T1DM) or type 2 diabetes (T2DM) are to avoid disruption of stable metabolic state, maintain optimal glycemic control, and prevent adverse glycemic events. Patients with T1DM require insulin therapy at all times to prevent ketosis. The management strategies for patients with T2DM include temporary discontinuation of certain oral antidiabetic agents and consideration for insulin therapy. Patients with T2DM who are relatively stable and able to eat regularly may continue with oral antidiabetic agents if glycemic control is satisfactory. Hyperglycemia may develop in patients with systemic corticosteroid treatment and should be managed upon accordingly.

An Artificial Neural Network-based Predictive Model to Support Optimization of Inpatient Glycemic Control

Background: Achieving glycemic control in critical care patients is of paramount importance, and has been linked to reductions in mortality, intensive care unit (ICU) length of stay, and morbidities such as infection. The myriad of illnesses and patient conditions render maintenance of glycemic control very challenging in this setting. Materials and Methods: This study involved collection of continuous glucose monitoring (CGM) data, and other associated measures, from the electronic medical records of 127 patients for the first 72 h of ICU care who upon admission to the ICU had a diagnosis of type 1 (n = 8) or type 2 diabetes (n = 97) or a glucose value >150 mg/dL (n = 22). A neural network-based model was developed to predict a complete trajectory of glucose values up to 135 min ahead of time. Model accuracy was validated using data from 15 of the 127 patients who were not included in the model training set to simulate model performance in real-world health care settings. Results: Predictive models achieved an improved accuracy and performance compared with previous models that were reported by our research team. Model error, expressed as mean absolute difference percent, was 10.6% with respect to interstitial glucose values (CGM) and 15.9% with respect to serum blood glucose values collected 135 min in the future. A Clarke Error Grid Analysis of model predictions with respect to the reference CGM and blood glucose measurements revealed that >99% of model predictions could be regarded as clinically acceptable and would not lead to inaccurate insulin therapy or treatment recommendations. Conclusion: The noted clinical acceptability of these models illustrates their potential utility within a clinical decision support system to assist health care providers in the optimization of glycemic management in critical care patients.

Diabetes Technology in the Inpatient Setting for Management of Hyperglycemia

In past decades, a rapid evolution of diabetes technology led to increased popularity and use of continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) in the ambulatory setting for diabetes management, and recently, the artificial pancreas became available. Efforts to translate this technology to the hospital setting have shown accuracy and reliability of CGM, safety of CSII in appropriate populations, improvement of inpatient glycemic control with computerized glycemic management systems, and feasibility of inpatient CGM-CSII closed-loop systems. Several ongoing studies are focusing on continued translation of this technology to improve glycemic control and outcomes in hospitalized patients.

Continuous glucose monitoring system can improve the quality of glucose control and glucose variability compared with point-of-care measurement in critically ill patients: A randomized controlled trial

BACKGROUND

The purpose of this study was to determine whether subcutaneous continuous glucose monitoring systems (CGMS) could improve glucose management in critically ill patients compared with frequent and conventional point-of-care (POC) glucose measurements.

METHODS

A total of 144 patients with an expected length of stay in the ICU of at least 72 hours and with an admission glucose or two random glucose values of >10.0 mmol/L within 24 hours after admission, were randomly assigned to the CGMS group (n = 74) or the conventional group (C group, n = 70). Both groups used the same insulin algorithm to reach the same glucose target range (8.0-10.0 mmol/L).

RESULTS

Time in range (TIR, 8.0-10.0 mmol/L), which is our primary outcome measure, was higher in the CGMS group than in the C group (51.5% vs. 29.0%, P < .001). Glucose variability (coefficient of variation, CV; standard deviation, SD; glucose lability index, and GLI) was improved by CGMS (all P < .05). Mean glucose level (MGL) (9.6 vs. 10.3 mmol/L, P = .156) and the proportion of patients with hypoglycemia did not differ between CGMS (5.4%) and C (5.7%) (P = 1.000). However, duration of hypoglycemia was reduced in the CGMS group (15 vs. 28 minutes, P = .032). Clinical outcomes were similar between groups except for the fewer usage of CRRT and lower peak plasma urea nitrogen level in the CGMS group.

CONCLUSION

The use of CGMS, compared with POC glucose measurement, could improve the TIR, GV and duration of hypoglycemia.

Designing the Glucose Telemetry for Hospital Management: From Bedside to the Nursing Station

PURPOSE OF THE REVIEW

Hospitalized patients with diabetes are monitored with point-of-care glucose testing. Continuous glucose monitoring (CGM) devices represent an alternative way to monitor glucose values; however, the in-hospital CGM use is still considered experimental. Most inpatient studies used "blinded" CGM properties and only few used the real-time/unblinded CGM features. One major limitation of the CGM devices is that they need to be placed at the patients' bedside, limiting any therapeutic interventions. In this article, we review the real-time/unblinded CGM use and share our thoughts about the development of future inpatient CGM systems.

RECENT FINDINGS

We recently reported that glucose values can be wirelessly transmitted to the nursing station, providing remote continuous glucose monitoring. Future inpatient CGM devices may be utilized for patients at risk for hypoglycemia similarly to the way that we use cardiac telemetry to monitor hospitalized patients who are at increased risk for cardiac arrhythmias.

Diabetes Technology Update: Use of Insulin Pumps and Continuous Glucose Monitoring in the Hospital

The use of continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) systems has gained wide acceptance in diabetes care. These devices have been demonstrated to be clinically valuable, improving glycemic control and reducing risks of hypoglycemia in ambulatory patients with type 1 diabetes and type 2 diabetes. Approximately 30-40% of patients with type 1 diabetes and an increasing number of insulin-requiring patients with type 2 diabetes are using pump and sensor technology. As the popularity of these devices increases, it becomes very likely that hospital health care providers will face the need to manage the inpatient care of patients under insulin pump therapy and CGM. The American Diabetes Association advocates allowing patients who are physically and mentally able to continue to use their pumps when hospitalized. Health care institutions must have clear policies and procedures to allow the patient to continue to receive CSII treatment to maximize safety and to comply with existing regulations related to self-management of medication. Randomized controlled trials are needed to determine whether CSII therapy and CGM systems in the hospital are associated with improved clinical outcomes compared with intermittent monitoring and conventional insulin treatment or with a favorable cost-benefit ratio.

Comparison of Space Glucose Control and Routine Glucose Management Protocol for Glycemic Control in Critically Ill Patients: A Prospective, Randomized Clinical Study

Background:

The Space Glucose Control (SGC) system is a computer-assisted device combining infusion pumps with the enhanced Model Predictive Control algorithm to achieve the target blood glucose (BG) level safely. The objective of this study was to evaluate the efficacy and safety of glycemic control by SGC with customized BG target range of 5.8–8.9 mmol/L in the critically ill patients.

Methods:

It is a randomized controlled trial of seventy critically ill patients with mechanical ventilation and hyperglycemia (BG ≥ 9.0 mmol/L). Thirty-six patients in the SGC group and 34 in the routine glucose management group were observed for three consecutive days. Target BG for both groups was 5.8–8.9 mmol/L. The primary outcome was the percentage time in the target range.

Results:

The percentage time within BG target range in the SGC group (69 ± 15%) was significantly higher than in the routine management group (52 ± 24%; P < 0.01). No measurement was ≤2.2 mmol/L, and there was only one episode of hypoglycemia (2.3–3.3 mmol/L) in each group. The average BG was significantly lower in the SGC group (7.8 ± 0.7 mmol/L) than in the routine management group (9.1 ± 1.6 mmol/L, P < 0.001). Target BG level was reached earlier in the SGC group than routine management group (2.5 ± 2.9 vs. 12.1 ± 15.3 h, P = 0.001). However, the SGC group performed worse for daily insulin requirement (59.8 ± 39.3 vs. 28.4 ± 36.7 U, P = 0.001) and sampling interval (2.0 ± 0.5 vs. 3.7 ± 0.5 h, P < 0.001) than the routine management group did. Multiple linear regression showed that the intervention group remained a significant individual predictor (P < 0.001) of the percentage time in target range.

Conclusions:

The SGC system, with a BG target of 5.8–8.9 mmol/L, resulted in effective and reliable glycemic control with few hypoglycemic episodes in critically ill patients with mechanical ventilation and hyperglycemia. However, the workload was increased.

Trial Registration:

http://www.clinicaltrials.gov, NCT 02491346; https://www.clinicaltrials.gov/ct2/show/NCT02491346?term=NCT02491346&cond=Hyperglycemia&cntry1=ES%3ACN&rank=1.

Fully Closed-Loop Multiple Model Probabilistic Predictive Controller Artificial Pancreas Performance in Adolescents and Adults in a Supervised Hotel Setting

BACKGROUND

Initial Food and Drug Administration-approved artificial pancreas (AP) systems will be hybrid closed-loop systems that require prandial meal announcements and will not eliminate the burden of premeal insulin dosing. Multiple model probabilistic predictive control (MMPPC) is a fully closed-loop system that uses probabilistic estimation of meals to allow for automated meal detection. In this study, we describe the safety and performance of the MMPPC system with announced and unannounced meals in a supervised hotel setting.

RESEARCH DESIGN AND METHODS

The Android phone-based AP system with remote monitoring was tested for 72 h in six adults and four adolescents across three clinical sites with daily exercise and meal challenges involving both three announced (manual bolus by patient) and six unannounced (no bolus by patient) meals. Safety criteria were predefined. Controller aggressiveness was adapted daily based on prior hypoglycemic events.

RESULTS

Mean 24-h continuous glucose monitor (CGM) was 157.4 ± 14.4 mg/dL, with 63.6 ± 9.2% of readings between 70 and 180 mg/dL, 2.9 ± 2.3% of readings <70 mg/dL, and 9.0 ± 3.9% of readings >250 mg/dL. Moderate hyperglycemia was relatively common with 24.6 ± 6.2% of readings between 180 and 250 mg/dL, primarily within 3 h after a meal. Overnight mean CGM was 139.6 ± 27.6 mg/dL, with 77.9 ± 16.4% between 70 and 180 mg/dL, 3.0 ± 4.5% <70 mg/dL, 17.1 ± 14.9% between 180 and 250 mg/dL, and 2.0 ± 4.5%> 250 mg/dL. Postprandial hyperglycemia was more common for unannounced meals compared with announced meals (4-h postmeal CGM 197.8 ± 44.1 vs. 140.6 ± 35.0 mg/dL; P < 0.001). No participants met safety stopping criteria.

CONCLUSIONS

MMPPC was safe in a supervised setting despite meal and exercise challenges. Further studies are needed in a less supervised environment.

Hypoglycemia Prevention by Algorithm Design During Intravenous Insulin Infusion

PURPOSE OF REVIEW

This review examines algorithm design features that may reduce risk for hypoglycemia while preserving glycemic control during intravenous insulin infusion. We focus principally upon algorithms in which the assignment of the insulin infusion rate (IR) depends upon maintenance rate of insulin infusion (MR) or a multiplier.

RECENT FINDINGS

Design features that may mitigate risk for hypoglycemia include use of a mid-protocol bolus feature and establishment of a low BG threshold for temporary interruption of infusion. Computer-guided dosing may improve target attainment without exacerbating risk for hypoglycemia. Column assignment (MR) within a tabular user-interpreted algorithm or multiplier may be specified initially according to patient characteristics and medical condition with revision during treatment based on patient response. We hypothesize that a strictly increasing sigmoidal relationship between MR-dependent IR and BG may reduce risk for hypoglycemia, in comparison to a linear relationship between multiplier-dependent IR and BG. Guidelines are needed that curb excessive up-titration of MR and recommend periodic pre-emptive trials of MR reduction. Future research should foster development of recommendations for "protocol maxima" of IR appropriate to patient condition.

Prednosti in slabosti različnih protokolov vodenja vrednosti glukoze v krvi pri kritično bolnih pacientih

Uvod: Medicinske sestre v enotah intenzivne terapije uravnavajo ciljno vrednost glukoze v krvi pri kritično bolnih po sprejetih in veljavnih protokolih. Namen raziskave je bil raziskati prednosti in slabosti različnih protokolov vodenja  vrednosti glukoze v krvi pri kritično bolnih.Metode: Uporabljen je bil sistematični pregled znanstvene in strokovne literature. Iskanje literature je potekalo od 1. 2. 2017 do 8. 8. 2017. V pregled so bile vključene naslednje baze: COBIB.SI, Digitalna knjižnica Slovenije – Dlib.si, CINAHL, ProQuest, PubMed in Google Učenjak. Iskanje je potekalo z različnimi kombinacijami ključnih besed v slovenskem in angleškem jeziku: prednosti, slabosti, medicinske sestre, kritično bolni, glukoza v krvi in protokoli za vodenje vrednosti glukoze v krvi. Uporabljen je bil Boolov operater AND. Iz iskalnega nabora 1064 zadetkov je bilo v končno analizo  vključenih 15 člankov. Za obdelavo podatkov je bil uporabljen model analize konceptov.Rezultati: Identificirana so bila tri tematska področja: (1) primernost različnih protokolov za vodenje vrednosti glukoze v krvi, (2) delovne obremenitve medicinskih sester pri teh protokolih in (3) varnost protokolov. Prednosti računalniško podprtega protokola za vodenje vrednosti glukoze v krvi so v boljšem doseganju ciljne vrednosti koncentracije glukoze v krvi, slabosti pa v pojavu odstopanj v zvezi z načrtovanim časom za merjenje glukoze v krvi.Diskusija in zaključek: Nekatere raziskave ugotavljajo prednosti računalniško podprtih protokolov za vodenje vrednosti glukoze v krvi v smislu tehnoloških izboljšav, zmanjšanja delovnih obremenitev medicinskih sester in izboljšanja varnosti pacientov. Raziskava prispeva k izboljševanju klinične prakse pri delu s kritično bolnimi pacienti.

Continuous Glucose Monitoring Measures Can Be Used for Glycemic Control in the ICU: An In-Silico Study

BACKGROUND

Continuous glucose monitoring (CGM) technology has become more prevalent in the intensive care unit (ICU), offering potential benefits of increased safety and reduced workload in glycemic control (GC). The drift and higher point accuracy errors of CGM devices over traditional intermittent blood glucose (BG) measures have so far limited their application in the ICU. This study delineates the trade-offs of performance, safety and workload that CGM sensors provide in GC protocols.

METHODS

Clinical data from 236 patients were used for clinically validated virtual trials. A CGM-enabled version of the STAR GC protocol was used to evaluate the use of guard rails and rolling windows. Safety was assessed through percentage of patients who had a severe hypoglycemic episode (BG < 40 mg/dl) as well as percentage of resampled BG < 72 mg/dl. Performance was assessed as percentage of resampled measurements in the 80-126 mg/dl and the 80-144 mg/dl target bands. Workload was measured by number of manual BG measures per day.

RESULTS

CGM-enabled versions of STAR decreased the number of required blood draws by up to 74%, while maintaining performance (76.6% BG measurements in the 80-126 mg/dl range vs 62.8% clinically, 87.9% in the 80-144 mg/dl range vs 83.7% clinically) and maintaining patient safety (1.13% of patients experienced a severe hypoglycemic event vs 0.85% clinically, 1.37% of BG measurements were less than 72 mg/dl vs 0.51% clinically).

CONCLUSION

CGM sensor traces were reproduced in virtual trials to guide GC. Existing GC protocols such as STAR may need to be adjusted only slightly to gain the benefits of the increased temporal measurements of CGM sensors, through which workload may be significantly decreased while maintaining GC performance and safety.

Accuracy and reliability of a subcutaneous continuous glucose monitoring device in critically ill patients

Subcutaneous continuous glucose monitoring (CGM) may have benefits in achieving glycemic control in critically ill patients. The aim of this study was to assess the accuracy and reliability of the FreeStyle Navigator I in critically ill patients and to assess patient related factors influencing the accuracy and reliability. This study is a retrospective analysis of data from a randomized controlled trial conducted in a 20-bed mixed intensive care unit. Analytical accuracy, clinical accuracy and reliability were assessed against arterial blood glucose samples as reference. Assessment was according to recent consensus recommendations with median absolute relative difference (median ARD), Bland-Altman plots, the ISO system accuracy standards (ISO 15197:2013) and Clarke error grid analysis (CEG). We analyzed 2840 paired measurements from 155 critically ill patients. The median ARD of all paired values was 13.3 [6.9-22.1]%. The median ARD was significantly higher in both the hypoglycemic and the hyperglycemic range (32.4 [12.1-53.4]% and 18.7 [10.7-28.3]% respectively, p < 0.001). The Bland-Altman analysis showed a mean bias of - 0.82 mmol/L with a lower limit of agreement (LOA) of - 3.88 mmol/L and an upper LOA of 2.24 mmol/L. A total of 1626 (57.3%) values met the ISO-2013, standards and 1,334 (47%) CGM values were within 12.5% from the reference value. CEG: 71.0% zone A, 25.8% zone B, 0.5% zone C, 2.5% zone D, 0.3% zone E. The median overall real-time data display time was 94.0 ± 14.9% and in 23% of the patients, the sensor measured < 95% of the time. Additionally, data gaps longer than 30 min were found in 48% of the patients. The analytical accuracy of the FreeStyle Navigator I in critically ill patients was suboptimal. Furthermore, the clinical accuracy, did not meet the required standards. The reliability was satisfactory, however, in almost a quarter of the patients the realtime data display was < 95%. The accuracy was considerably and significantly lower in hyper- and hypoglycemic ranges.

Keeping Up with the Diabetes Technology: 2016 Endocrine Society Guidelines of Insulin Pump Therapy and Continuous Glucose Monitor Management of Diabetes

PURPOSE OF REVIEW

Decades after the invention of insulin pump, diabetes management has encountered a technology revolution with the introduction of continuous glucose monitoring, sensor-augmented insulin pump therapy and closed-loop/artificial pancreas systems. In this review, we discuss the significance of the 2016 Endocrine Society Guidelines for insulin pump therapy and continuous glucose monitoring and summarize findings from relevant diabetes technology studies that were conducted after the publication of the 2016 Endocrine Society Guidelines.

RECENT FINDINGS

The 2016 Endocrine Society Guidelines have been a great resource for clinicians managing diabetes in this new era of diabetes technology. There is good body of evidence indicating that using diabetes technology systems safely tightens glycemic control while managing both type 1 and type 2 diabetes. The first-generation diabetes technology systems will evolve as we gain more experience and collaboratively work to improve them with an ultimate goal of keeping people with diabetes complication and burden-free until the cure for diabetes becomes a reality.

Consensus Statement on Inpatient Use of Continuous Glucose Monitoring

In June 2016, Diabetes Technology Society convened a panel of US experts in inpatient diabetes management to discuss the current and potential role of continuous glucose monitoring (CGM) in the hospital. This discussion combined with a literature review was a follow-up to a meeting, which took place in May 2015. The panel reviewed evidence on use of CGM in 3 potential inpatient scenarios: (1) the intensive care unit (ICU), (2) non-ICU, and (3) transitioning outpatient CGM use into the hospital setting. Panel members agreed that data from limited studies and theoretical considerations suggested that use of CGM in the hospital had the potential to improve patient clinical outcomes, and in particular reduction of hypoglycemia. Panel members discussed barriers to widespread adoption of CGM, which patients would benefit most from use of this technology, and what type of outcome studies are needed to guide use of CGM in the inpatient setting.

Inpatient Continuous Glucose Monitoring and Glycemic Outcomes

Continuous glucose monitoring (CGM) is commonly used in the outpatient setting to improve diabetes management. CGM can provide real-time glucose trends, detecting hyperglycemia and hypoglycemia before the onset of clinical symptoms. In 2011, at the time the Endocrine Society CGM guidelines were published, the society did not recommend inpatient CGM as its efficacy and safety were unknown. While many studies have subsequently evaluated inpatient CGM accuracy and reliability, glycemic outcome studies have not been widely published. In the non-ICU setting, investigational CGM studies have commonly blinded providers and patients to glucose data. Retrospective review of the glucose data reflects increased hypoglycemia detection with CGM. In the ICU setting, data are inconsistent whether CGM can improve glycemic outcomes. Studies have not focused on hospitalized patients with type 1 diabetes mellitus, the population most likely to benefit from inpatient CGM. This article reviews inpatient CGM glycemic outcomes in the non-ICU and ICU setting.

The Clinical Benefits and Accuracy of Continuous Glucose Monitoring Systems in Critically Ill Patients-A Systematic Scoping Review

Continuous Glucose Monitoring (CGM) systems could improve glycemic control in critically ill patients. We aimed to identify the evidence on the clinical benefits and accuracy of CGM systems in these patients. For this, we performed a systematic search in Ovid MEDLINE, from inception to 26 July 2016. Outcomes were efficacy, accuracy, safety, workload and costs. Our search retrieved 356 articles, of which 37 were included. Randomized controlled trials on efficacy were scarce (n = 5) and show methodological limitations. CGM with automated insulin infusion improved time in target and mean glucose in one trial and two trials showed a decrease in hypoglycemic episodes and time in hypoglycemia. Thirty-two articles assessed accuracy, which was overall moderate to good, the latter mainly with intravascular devices. Accuracy in critically ill children seemed lower than in adults. Adverse events were rare. One study investigated the effect on workload and cost, and showed a significant reduction in both. In conclusion, studies on the efficacy and accuracy were heterogeneous and difficult to compare. There was no consistent clinical benefit in the small number of studies available. Overall accuracy was moderate to good with some intravascular devices. CGM systems seemed however safe, and might positively affect workload and costs.

Round Table Discussion on Inpatient Use of Continuous Glucose Monitoring at the International Hospital Diabetes Meeting

In May 2015 the Diabetes Technology Society convened a panel of 27 experts in hospital medicine and endocrinology to discuss the current and potential future roles of continuous glucose monitoring (CGM) in delivering optimum health care to hospitalized patients in the United States. The panel focused on 3 potential settings for CGM in the hospital, including (1) the intensive care unit (ICU), (2) non-ICU, and (3) continuation of use of home CGM in the hospital. The group reviewed barriers to use and solutions to overcome the barriers. They concluded that CGM has the potential to improve the quality of patient care and can provide useful information to help health care providers learn more about glucose management. Widespread adoption of CGM by hospitals, however, has been limited by added costs and insufficient outcome data.

Space GlucoseControl system for blood glucose control in intensive care patients--a European multicentre observational study

BACKGROUND

Glycaemia control (GC) remains an important therapeutic goal in critically ill patients. The enhanced Model Predictive Control (eMPC) algorithm, which models the behaviour of blood glucose (BG) and insulin sensitivity in individual ICU patients with variable blood samples, is an effective, clinically proven computer based protocol successfully tested at multiple institutions on medical and surgical patients with different nutritional protocols. eMPC has been integrated into the B.Braun Space GlucoseControl system (SGC), which allows direct data communication between pumps and microprocessor. The present study was undertaken to assess the clinical performance and safety of the SGC for glycaemia control in critically ill patients under routine conditions in different ICU settings and with various nutritional protocols.

METHODS

The study endpoints were the percentage of time the BG was within the target range 4.4 - 8.3 mmol.l(-1), the frequency of hypoglycaemic episodes, adherence to the advice of the SGC and BG measurement intervals. BG was monitored, and insulin was given as a continuous infusion according to the advice of the SGC. Nutritional management (enteral, parenteral or both) was carried out at the discretion of each centre.

RESULTS

17 centres from 9 European countries included a total of 508 patients, the median study time was 2.9 (1.9-6.1) days. The median (IQR) time-in-target was 83.0 (68.7-93.1) % of time with the mean proposed measurement interval 2.0 ± 0.5 hours. 99.6% of the SGC advices on insulin infusion rate were accepted by the user. Only 4 episodes (0.01% of all BG measurements) of severe hypoglycaemia <2.2 mmol.l(-1) in 4 patients occurred (0.8%; 95% CI 0.02-1.6%).

CONCLUSION

Under routine conditions and under different nutritional protocols the Space GlucoseControl system with integrated eMPC algorithm has exhibited its suitability for glycaemia control in critically ill patients.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01523665.

Vascular Glucose Sensor Symposium: Continuous Glucose Monitoring Systems (CGMS) for Hospitalized and Ambulatory Patients at Risk for Hyperglycemia, Hypoglycemia, and Glycemic Variability

Hyperglycemia, hypoglycemia, and glycemic variability have been associated with increased morbidity, mortality, length of stay, and cost in a variety of critical care and non-critical care patient populations in the hospital. The results from prospective randomized clinical trials designed to determine the risks and benefits of intensive insulin therapy and tight glycemic control have been confusing; and at times conflicting. The limitations of point-of-care blood glucose (BG) monitoring in the hospital highlight the great clinical need for an automated real-time continuous glucose monitoring system (CGMS) that can accurately measure the concentration of glucose every few minutes. Automation and standardization of the glucose measurement process have the potential to significantly improve BG control, clinical outcome, safety and cost.

Glucose control in the intensive care unit by use of continuous glucose monitoring: what level of measurement error is acceptable?

BACKGROUND

Accuracy and frequency of glucose measurement is essential to achieve safe and efficacious glucose control in the intensive care unit. Emerging continuous glucose monitors provide frequent measurements, trending information, and alarms. The objective of this study was to establish the level of accuracy of continuous glucose monitoring (CGM) associated with safe and efficacious glucose control in the intensive care unit.

METHODS

We evaluated 3 established glucose control protocols [Yale, University of Washington, and Normoglycemia in Intensive Care Evaluation and Surviving Using Glucose Algorithm Regulation (NICE-SUGAR)] by use of computer simulations. Insulin delivery was informed by intermittent blood glucose (BG) measurements or CGM levels with an increasing level of measurement error. Measures of glucose control included mean glucose, glucose variability, proportion of time glucose was in target range, and hypoglycemia episodes.

RESULTS

Apart from the Washington protocol, CGM with mean absolute relative deviation (MARD) ≤ 15% resulted in similar mean glucose as with the use of intermittent BG measurements. Glucose variability was also similar between CGM and BG-informed protocols. Frequency and duration of hypoglycemia were not worse by use of CGM with MARD ≤ 10%. Measures of glucose control varied more between protocols than at different levels of the CGM error.

CONCLUSIONS

The efficacy of CGM-informed and BG-informed commonly used glucose protocols is similar, but the risk of hypoglycemia may be reduced by use of CGM with MARD ≤ 10%. Protocol choice has greater influence on glucose control measures than the glucose measurement method.

Continuous glucose monitoring in insulin-treated patients in non-ICU settings

Inpatient hyperglycemia, in patients with and without a history of diabetes, is associated with increased risk of complications, mortality, and longer hospital stay in medicine and surgical patients. Bedside capillary point of care testing is widely recommended as the preferred method for glucose monitoring and for guiding glycemic management of individual patients; however, the accuracy of most handheld glucose meters is far from optimal. Recent studies in the hospital setting have reported that the use of continuous glucose monitoring (CGM) can provide real-time information about glucose concentration, direction, and rate of change over a period of several days. Because it provides glucose values every 5-10 minutes 24 hours a day, CGM may have an advantage over point of care testing with respect to reducing the incidence of severe hypoglycemia in acute care. Real-time CGM technology may facilitate glycemic control and to reduce hypoglycemia in insulin-treated patients. Recent guidelines, however, have recommended deferring the use of CGM in the adult hospital setting until further data on accuracy and safety become available. In this study, we review the advantages and disadvantages of the use of real-time CGM in the management of dysglycemia in the hospital setting.

Glucose control in intensive care: usability, efficacy and safety of Space GlucoseControl in two medical European intensive care units

BACKGROUND

The Space GlucoseControl system (SGC) is a nurse-driven, computer-assisted device for glycemic control combining infusion pumps with the enhanced Model Predictive Control algorithm (B. Braun, Melsungen, Germany). We aimed to investigate the performance of the SGC in medical critically ill patients.

METHODS

Two open clinical investigations in tertiary centers in Graz, Austria and Zurich, Switzerland were performed. Efficacy was assessed by percentage of time within the target range (4.4-8.3 mmol/L; primary end point), mean blood glucose, and sampling interval. Safety was assessed by the number of hypoglycemic episodes (≤2.2 mmol/L) and the percentage of time spent below this cutoff level. Usability was analyzed with a standardized questionnaire given to involved nursing staff after the trial.

RESULTS

Forty medical critically ill patients (age, 62 ± 15 years; body mass index, 30.0 ± 8.9 kg/m2; APACHE II score, 24.8 ± 5.4; 27 males; 8 with diabetes) were included for a period of 6.5 ± 3.7 days (n = 20 in each center). The primary endpoint (time in target range 4.4 to 8.3 mmol/l) was reached in 88.3% ± 9.3 of the time and mean arterial blood glucose was 6.7 ± 0.4 mmol/l. The sampling interval was 2.2 ± 0.4 hours. The mean daily insulin dose was 87.2 ± 64.6 IU. The adherence to the given insulin dose advice was high (98.2%). While the percentage of time spent in a moderately hypoglycemic range (2.2 to 3.3 mmol/L) was low (0.07 ± 0.26% of the time), one severe hypoglycemic episode (<2.2 mmol/L) occurred (2.5% of patients or 0.03% of glucose readings).

CONCLUSIONS

SGC is a safe and efficient method to control blood glucose in critically ill patients as assessed in two European medical intensive care units.

Glucose control in the ICU: is there a time for more ambitious targets again?

During the last 2 decades, the treatment of hyperglycemia in critically ill patients has become one of the most discussed topics in the intensive medicine field. The initial data suggesting significant benefit of normalization of blood glucose levels in critically ill patients using intensive intravenous insulin therapy have been challenged or even neglected by some later studies. At the moment, the need for glucose control in critically ill patients is generally accepted yet the target glucose values are still the subject of ongoing debates. In this review, we summarize the current data on the benefits and risks of tight glucose control in critically ill patients focusing on the novel technological approaches including continuous glucose monitoring and its combination with computer-based algorithms that might help to overcome some of the hurdles of tight glucose control. Since increased risk of hypoglycemia appears to be the major obstacle of tight glucose control, we try to put forward novel approaches that may help to achieve optimal glucose control with low risk of hypoglycemia. If such approaches can be implemented in real-world practice the entire concept of tight glucose control may need to be revisited.

Glucose control in non-critically ill inpatients with diabetes: towards closed-loop

Inpatient glycaemic control remains an important issue due to the increasing number of patients with diabetes admitted to hospital. Morbidity and mortality in hospital are associated with poor glucose control, and cost of hospitalization is higher compared to non-diabetes patients. Guidelines for inpatient glycaemic control in the non-critical care setting have been published. Current recommendations include basal-bolus insulin therapy, regular glucose monitoring, as well as enhancing healthcare provider's role and knowledge. In spite of growing focus, implementation in practice is limited, mainly due to increasing workload burden on staff and fear of hypoglycaemia. Advances in healthcare technology may contribute to an improvement of inpatient diabetes care. Integration of glucose measurements with healthcare records and computerized glycaemic control protocols are currently being used in some institutions. Recent interests in continuous glucose monitoring have led to studies assessing its utilization in inpatients. Automation of glucose monitoring and insulin delivery may provide a safe and efficacious tool for hospital staff to manage inpatient hyperglycaemia, whilst reducing staff workload. This review summarizes the evidence on current approaches to managing inpatient glycaemic control; its utility and limitations. We conclude by discussing the evidence from feasibility studies to date, on the potential use of closed loop in the non-critical care setting and its implication for future studies.

A quadruply-asymmetric sigmoid to describe the insulin-glucose relationship during intravenous insulin infusion

For hospitalized patients requiring intravenous insulin therapy, an objective is to quantify the intravenous insulin infusion rate (IR) across the domain of blood glucose (BG) values at a single timepoint. The algorithm parameters include low BG (70 mg/dL), critical high BG, target range BG limits, and maintenance rate (MR) of insulin infusion, which, after initialization, depends on rate of change of blood glucose, previous IR, and other inputs. The restraining rate (RR) is a function of fractional completeness of ascent of BG (FCABG) from BG 70 mg/dL to target. The correction rate (CR) is a function of fractional elevation of BG (FEBG), in comparison to elevation of a critical high BG, above target. IR = RR + CR. The proposed mathematical model describing a sigmoidal relationship between IR and BG may offer a safety advantage over the linear relationship currently employed in some intravenous glucose management systems.